matlab计算面积和形心源码

说明：  配网系统中的无功功率 优化，粒子群算法，Matlab(Reactive power optimization, particle swarm optimization, matlab)

DaWang PHP自适应博客系统源程序，这是我学习php来做的第一个demo，不容易啊，万事开头难，知道有很多不足还需改进。首页的大图在ad.php文件修改。我本来想放进一个编辑器的，结果想想和改文件没啥区别，争取以后把这个功能加上。更改标题是在head.php这个文件里边。 　　安装的话是访问 你的网址/setup 　　默认帐号：admin 密码：admin888 后台可以直接修改！

2018认证杯数学建模A题论文一等奖，最终获得一等奖(2018 certification cup mathematical modeling first prize paper)

计算机组成原理对于程序员非常重要，这是一本关于计算机组成原理的书籍。(The principles of computer composition are very important to programmers. This is a book about the principles of computer composition.)

电机模型simulink，搭建了基本的模型仿真(MC simulink Build a basic model simulation)

说明：  基于卷积神经网络和互联网新闻文本的股票趋势预测代码(Stock trend prediction code based on convolutional neural network and Internet news text)

说明：  cohen大神的OM-LSA仿真代码 MATLAB software for speech enhancement based on optimally modified LSA (OM-LSA) speech estimator and improved minima controlled recursive averaging (IMCRA) noise estimation approach for robust speech enhancement.(MATLAB software for speech enhancement based on optimally modified LSA (OM-LSA) speech estimator and improved minima controlled recursive averaging (IMCRA) noise estimation approach for robust speech enhancement. The algorithms are described in: I. Cohen and B. Berdugo, Speech Enhancement for Non-Stationary Noise Environments, Signal Processing, Vol. 81, No. 11, Nov. 2001, pp. 2403-2418. I. Cohen, Noise Spectrum Estimation in Adverse Environments: Improved Minima Controlled Recursive Averaging, IEEE Trans. Speech and Audio Processing, Vol. 11, No. 5, Sep. 2003, pp. 466-475. I. Cohen and S. Gannot, Spectral Enhancement Methods, in Jacob Benesty, M. Mohan Sondhi and Yiteng (Arden) Huang (Eds.), Springer Handbook of Speech Processing, Springer, 2008, Part H, Ch. 44, pp. 873-901)

说明：  高斯光束经过厚透镜的光强分布，平凸透镜，1mm半径(Intensity distribution of Gaussian beam passing through thick lens)

使用VC实现的 连连看 所有源代码以及资源文件(Implemented using VC Lianliankan all source code and resource files)

说明：  计算kl散度，信息熵，是随机变量或整个系统的不确定性。熵越大，随机变量或系统的不确定性就越大。 相对熵，用来衡量两个取值为正的函数或概率分布之间的差异。 交叉熵，用来衡量在给定的真实分布下，使用非真实分布所指定的策略消除系统的不确定性所需要付出的努力的大小。 相对熵=交叉熵-信息熵(Relative entropy, used to measure the difference between two positive functions or probability distributions. Cross entropy is used to measure the size of the effort required to eliminate the uncertainty of the system by using the strategy specified by the non real distribution in a given real distribution.)